Study that will be conducted through an exploratory research design for empirical evidence studies aimed at exploring the application of ARIMA on predicting and forecasting variables. In addition, the study need to be developed through secondary data obtained from review of literature relevant and related to the topic of ARIMA model. To ensure validity, accuracy, and reliability of the findings, the literature for review should be selected based on a criterion requiring that all sources be relevant, recent and peer reviewed. Moreover, data obtained from the literature review will be analyzed through content analysis in order to draw important findings and conclusions from the study.
Resources:
Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs) – ScienceDirect
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting – ScienceDirect
A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting – ScienceDirect
Time series forecasting using a hybrid ARIMA and neural network model – ScienceDirect
Combining neural network model with seasonal time series ARIMA model – ScienceDirect
A fuzzy seasonal ARIMA model for forecasting – ScienceDirect
Mutivariate ARIMA forecasting of irregular time series – ScienceDirect
Artificial Neural Networks and ARIMA-Models within the Field of Stock Market Prediction – A Comparison – ScienceDirect